Exploring nasopharyngeal microbiota profile in children affected by SARS-CoV-2 infection

The relationship between COVID-19 and nasopharyngeal (NP) microbiota has been investigated mainly in the adult population. We explored the NP profile of children affected by COVID-19, compared to healthy controls (CTRLs). NP swabs of children with COVID-19, collected between March and September 2020, were investigated at the admission (T0), 72 h to 7 days (T1), and at the discharge (T2) of the patients. NP microbiota was analyzed by 16S rRNA targeted-metagenomics. Data from sequencing were investigated by QIIME 2.0 and PICRUSt 2. Multiple machine learning (ML) models were exploited to classify patients compared to CTRLs. The NP microbiota of COVID-19 patients (N = 71) was characterized by reduction of α-diversity compared to CTRLs (N = 59). The NP microbiota of COVID-19 cohort appeared significantly enriched in Streptococcus, Haemophilus, Staphylococcus, Veillonella, Enterococcus, Neisseria, Moraxella, Enterobacteriaceae, Gemella, Bacillus, and reduced in Faecalibacterium, Akkermansia, Blautia, Bifidobacterium, Ruminococcus, and Bacteroides, compared to CTRLs (FDR < 0.001). Exploiting ML models, Enterococcus, Pseudomonas, Streptococcus, Capnocytopagha, Tepidiphilus, Porphyromonas, Staphylococcus, and Veillonella resulted as NP microbiota biomarkers, in COVID-19 patients. No statistically significant differences were found comparing the NP microbiota profile of COVID-19 patients during the time-points or grouping patients on the basis of high, medium, and low viral load (VL). This evidence provides specific pathobiont signatures of the NP microbiota in pediatric COVID-19 patients, and the reduction of anaerobic protective commensals. Our data suggest that the NP microbiota may have a specific disease-related signature since infection onset without changes during disease progression, regardless of the SARS-CoV-2 VL.

IMPORTANCE

Since the beginning of pandemic, we know that children are less susceptible to severe COVID-19 disease. A potential role of the nasopharyngeal (NP) microbiota has been hypothesized but to date, most of the studies have been focused on adults. We studied the NP microbiota modifications in children affected by SARS-CoV-2 infection showing a specific NP microbiome profile, mainly composed by pathobionts and almost missing protective anaerobic commensals. Moreover, in our study, specific microbial signatures appear since the first days of infection independently from SARS-CoV-2 viral load.

Gut microbiota functional profiling in autism spectrum disorders: bacterial VOCs and related metabolic pathways acting as disease biomarkers and predictors

Background

Autism spectrum disorder (ASD) is a multifactorial neurodevelopmental disorder. Major interplays between the gastrointestinal (GI) tract and the central nervous system (CNS) seem to be driven by gut microbiota (GM). Herein, we provide a GM functional characterization, based on GM metabolomics, mapping of bacterial biochemical pathways, and anamnestic, clinical, and nutritional patient metadata.

Methods

Fecal samples collected from children with ASD and neurotypical children were analyzed by gas-chromatography mass spectrometry coupled with solid phase microextraction (GC-MS/SPME) to determine volatile organic compounds (VOCs) associated with the metataxonomic approach by 16S rRNA gene sequencing. Multivariate and univariate statistical analyses assessed differential VOC profiles and relationships with ASD anamnestic and clinical features for biomarker discovery. Multiple web-based and machine learning (ML) models identified metabolic predictors of disease and network analyses correlated GM ecological and metabolic patterns.

Results

The GM core volatilome for all ASD patients was characterized by a high concentration of 1-pentanol, 1-butanol, phenyl ethyl alcohol; benzeneacetaldehyde, octadecanal, tetradecanal; methyl isobutyl ketone, 2-hexanone, acetone; acetic, propanoic, 3-methyl-butanoic and 2-methyl-propanoic acids; indole and skatole; and o-cymene. Patients were stratified based on age, GI symptoms, and ASD severity symptoms. Disease risk prediction allowed us to associate butanoic acid with subjects older than 5 years, indole with the absence of GI symptoms and low disease severity, propanoic acid with the ASD risk group, and p-cymene with ASD symptoms, all based on the predictive CBCL-EXT scale. The HistGradientBoostingClassifier model classified ASD patients vs. CTRLs by an accuracy of 89%, based on methyl isobutyl ketone, benzeneacetaldehyde, phenyl ethyl alcohol, ethanol, butanoic acid, octadecane, acetic acid, skatole, and tetradecanal features. LogisticRegression models corroborated methyl isobutyl ketone, benzeneacetaldehyde, phenyl ethyl alcohol, skatole, and acetic acid as ASD predictors.

Conclusion

Our results will aid the development of advanced clinical decision support systems (CDSSs), assisted by ML models, for advanced ASD-personalized medicine, based on omics data integrated into electronic health/medical records. Furthermore, new ASD screening strategies based on GM-related predictors could be used to improve ASD risk assessment by uncovering novel ASD onset and risk predictors.

Williams-Beuren syndrome shapes the gut microbiota metaproteome

Williams-Beuren syndrome (WBS) is a rare genetic neurodevelopmental disorder with multi-systemic manifestations. The evidence that most subjects with WBS face gastrointestinal (GI) comorbidities, have prompted us to carry out a metaproteomic investigation of their gut microbiota (GM) profile compared to age-matched healthy subjects (CTRLs). Metaproteomic analysis was carried out on fecal samples collected from 41 individuals with WBS, and compared with samples from 45 CTRLs. Stool were extracted for high yield in bacterial protein group (PG) content, trypsin-digested and analysed by nanoLiquid Chromatography-Mass Spectrometry. Label free quantification, taxonomic assignment by the lowest common ancestor (LCA) algorithm and functional annotations by COG and KEGG databases were performed. Data were statistically interpreted by multivariate and univariate analyses. A WBS GM functional dissimilarity respect to CTRLs, regardless age distribution, was reported. The alterations in function of WBSs GM was primarily based on bacterial pathways linked to carbohydrate transport and metabolism and energy production. Influence of diet, obesity, and GI symptoms was assessed, highlighting changes in GM biochemical patterns, according to WBS subsets' stratification. The LCA-derived ecology unveiled WBS-related functionally active bacterial signatures: Bacteroidetes related to over-expressed PGs, and Firmicutes, specifically the specie Faecalibacterium prausnitzii, linked to under-expressed PGs, suggesting a depletion of beneficial bacteria. These new evidences on WBS gut dysbiosis may offer novel targets for tailored interventions.

Ecology and Machine Learning-Based Classification Models of Gut Microbiota and Inflammatory Markers May Evaluate the Effects of Probiotic Supplementation in Patients Recently Recovered from COVID-19

Gut microbiota (GM) modulation can be investigated as possible solution to enhance recovery after COVID-19. An open-label, single-center, single-arm, pilot, interventional study was performed by enrolling twenty patients recently recovered from COVID-19 to investigate the role of a mixed probiotic, containing Lactobacilli, Bifidobacteria and Streptococcus thermophilus, on gastrointestinal symptoms, local and systemic inflammation, intestinal barrier integrity and GM profile. Gastrointestinal Symptom Rating Scale, cytokines, inflammatory, gut permeability, and integrity markers were evaluated before (T0) and after 8 weeks (T1) of probiotic supplementation. GM profiling was based on 16S-rRNA targeted-metagenomics and QIIME 2.0, LEfSe and PICRUSt computational algorithms. Multiple machine learning (ML) models were trained to classify GM at T0 and T1. A statistically significant reduction of IL-6 (p < 0.001), TNF-α (p < 0.001) and IL-12RA (p < 0.02), citrulline (p value < 0.001) was reported at T1. GM global distribution and microbial biomarkers strictly reflected probiotic composition, with a general increase in Bifidobacteria at T1. Twelve unique KEGG orthologs were associated only to T0, including tetracycline resistance cassettes. ML classified the GM at T1 with 100% score at phylum level. Bifidobacteriaceae and Bifidobacterium spp. inversely correlated to reduction of citrulline and inflammatory cytokines. Probiotic supplementation during post-COVID-19 may trigger anti-inflammatory effects though Bifidobacteria and related-metabolism enhancement.

Multi-objective optimization determines when, which and how to fuse deep networks: An application to predict COVID-19 outcomes

The COVID-19 pandemic has caused millions of cases and deaths and the AI-related scientific community, after being involved with detecting COVID-19 signs in medical images, has been now directing the efforts towards the development of methods that can predict the progression of the disease. This task is multimodal by its very nature and, recently, baseline results achieved on the publicly available AIforCOVID dataset have shown that chest X-ray scans and clinical information are useful to identify patients at risk of severe outcomes. While deep learning has shown superior performance in several medical fields, in most of the cases it considers unimodal data only. In this respect, when, which and how to fuse the different modalities is an open challenge in multimodal deep learning. To cope with these three questions here we present a novel approach optimizing the setup of a multimodal end-to-end model. It exploits Pareto multi-objective optimization working with a performance metric and the diversity score of multiple candidate unimodal neural networks to be fused. We test our method on the AIforCOVID dataset, attaining state-of-the-art results, not only outperforming the baseline performance but also being robust to external validation. Moreover, exploiting XAI algorithms we figure out a hierarchy among the modalities and we extract the features' intra-modality importance, enriching the trust on the predictions made by the model.

The Relationship Between Pediatric Gut Microbiota and SARS-CoV-2 Infection

This is the first study on gut microbiota (GM) in children affected by coronavirus disease 2019 (COVID-19). Stool samples from 88 patients with suspected severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and 95 healthy subjects were collected (admission: 3–7 days, discharge) to study GM profile by 16S rRNA gene sequencing and relationship to disease severity. The study group was divided in COVID-19 (68), Non–COVID-19 (16), and MIS-C (multisystem inflammatory syndrome in children) (4). Correlations among GM ecology, predicted functions, multiple machine learning (ML) models, and inflammatory response were provided for COVID-19 and Non–COVID-19 cohorts. The GM of COVID-19 cohort resulted as dysbiotic, with the lowest α-diversity compared with Non–COVID-19 and CTRLs and by a specific β-diversity. Its profile appeared enriched in Faecalibacterium, Fusobacterium, and Neisseria and reduced in Bifidobacterium, Blautia, Ruminococcus, Collinsella, Coprococcus, Eggerthella, and Akkermansia, compared with CTRLs (p < 0.05). All GM paired-comparisons disclosed comparable results through all time points. The comparison between COVID-19 and Non–COVID-19 cohorts highlighted a reduction of Abiotrophia in the COVID-19 cohort (p < 0.05). The GM of MIS-C cohort was characterized by an increase of Veillonella, Clostridium, Dialister, Ruminococcus, and Streptococcus and a decrease of Bifidobacterium, Blautia, Granulicatella, and Prevotella, compared with CTRLs. Stratifying for disease severity, the GM associated to “moderate” COVID-19 was characterized by lower α-diversity compared with “mild” and “asymptomatic” and by a GM profile deprived in Neisseria, Lachnospira, Streptococcus, and Prevotella and enriched in Dialister, Acidaminococcus, Oscillospora, Ruminococcus, Clostridium, Alistipes, and Bacteroides. The ML models identified Staphylococcus, Anaerostipes, Faecalibacterium, Dorea, Dialister, Streptococcus, Roseburia, Haemophilus, Granulicatella, Gemmiger, Lachnospira, Corynebacterium, Prevotella, Bilophila, Phascolarctobacterium, Oscillospira, and Veillonella as microbial markers of COVID-19. The KEGG ortholog (KO)–based prediction of GM functional profile highlighted 28 and 39 KO-associated pathways to COVID-19 and CTRLs, respectively. Finally, Bacteroides and Sutterella correlated with proinflammatory cytokines regardless disease severity. Unlike adult GM profiles, Faecalibacterium was a specific marker of pediatric COVID-19 GM. The durable modification of patients’ GM profile suggested a prompt GM quenching response to SARS-CoV-2 infection since the first symptoms. Faecalibacterium and reduced fatty acid and amino acid degradation were proposed as specific COVID-19 disease traits, possibly associated to restrained severity of SARS-CoV-2–infected children. Altogether, this evidence provides a characterization of the pediatric COVID-19–related GM.

Gut Microbiota Ecology and Inferred Functions in Children With ASD Compared to Neurotypical Subjects

Autism spectrum disorders (ASDs) is a multifactorial neurodevelopmental disorder. The communication between the gastrointestinal (GI) tract and the central nervous system seems driven by gut microbiota (GM). Herein, we provide GM profiling, considering GI functional symptoms, neurological impairment, and dietary habits. Forty-one and 35 fecal samples collected from ASD and neurotypical children (CTRLs), respectively, (age range, 3–15 years) were analyzed by 16S targeted-metagenomics (the V3–V4 region) and inflammation and permeability markers (i.e., sIgA, zonulin lysozyme), and then correlated with subjects’ metadata. Our ASD cohort was characterized as follows: 30/41 (73%) with GI functional symptoms; 24/41 (58%) picky eaters (PEs), with one or more dietary needs, including 10/41 (24%) with food selectivity (FS); 36/41 (88%) presenting high and medium autism severity symptoms (HMASSs). Among the cohort with GI symptoms, 28/30 (93%) showed HMASSs, 17/30 (57%) were picky eaters and only 8/30 (27%) with food selectivity. The remaining 11/41 (27%) ASDs without GI symptoms that were characterized by HMASS for 8/11 (72%) and 7/11 (63%) were picky eaters. GM ecology was investigated for the overall ASD cohort versus CTRLs; ASDs with GI and without GI, respectively, versus CTRLs; ASD with GI versus ASD without GI; ASDs with HMASS versus low ASSs; PEs versus no-PEs; and FS versus absence of FS. In particular, the GM of ASDs, compared to CTRLs, was characterized by the increase of Proteobacteria, Bacteroidetes, Rikenellaceae, Pasteurellaceae, Klebsiella, Bacteroides, Roseburia, Lactobacillus, Prevotella, Sutterella, Staphylococcus, and Haemophilus. Moreover, Sutterella, Roseburia and Fusobacterium were associated to ASD with GI symptoms compared to CTRLs. Interestingly, ASD with GI symptoms showed higher value of zonulin and lower levels of lysozyme, which were also characterized by differentially expressed predicted functional pathways. Multiple machine learning models classified correctly 80% overall ASDs, compared with CTRLs, based on Bacteroides, Lactobacillus, Prevotella, Staphylococcus, Sutterella, and Haemophilus features. In conclusion, in our patient cohort, regardless of the evaluation of many factors potentially modulating the GM profile, the major phenotypic determinant affecting the GM was represented by GI hallmarks and patients’ age.

Pareto optimization of deep networks for COVID-19 diagnosis from chest X-rays

The year 2020 was characterized by the COVID-19 pandemic that has caused, by the end of March 2021, more than 2.5 million deaths worldwide. Since the beginning, besides the laboratory test, used as the gold standard, many applications have been applying deep learning algorithms to chest X-ray images to recognize COVID-19 infected patients. In this context, we found out that convolutional neural networks perform well on a single dataset but struggle to generalize to other data sources. To overcome this limitation, we propose a late fusion approach where we combine the outputs of several state-of-the-art CNNs, introducing a novel method that allows us to construct an optimum ensemble determining which and how many base learners should be aggregated. This choice is driven by a two-objective function that maximizes, on a validation set, the accuracy and the diversity of the ensemble itself. A wide set of experiments on several publicly available datasets, accounting for more than 92,000 images, shows that the proposed approach provides average recognition rates up to 93.54% when tested on external datasets.

AIforCOVID: Predicting the clinical outcomes in patients with COVID-19 applying AI to chest-X-rays. An Italian multicentre study

Recent epidemiological data report that worldwide more than 53 million people have been infected by SARS-CoV-2, resulting in 1.3 million deaths. The disease has been spreading very rapidly and few months after the identification of the first infected, shortage of hospital resources quickly became a problem. In this work we investigate whether artificial intelligence working with chest X-ray (CXR) scans and clinical data can be used as a possible tool for the early identification of patients at risk of severe outcome, like intensive care or death. Indeed, further to induce lower radiation dose than computed tomography (CT), CXR is a simpler and faster radiological technique, being also more widespread. In this respect, we present three approaches that use features extracted from CXR images, either handcrafted or automatically learnt by convolutional neuronal networks, which are then integrated with the clinical data. As a further contribution, this work introduces a repository that collects data from 820 patients enrolled in six Italian hospitals in spring 2020 during the first COVID-19 emergency. The dataset includes CXR images, several clinical attributes and clinical outcomes. Exhaustive evaluation shows promising performance both in 10-fold and leave-one-centre-out cross-validation, suggesting that clinical data and images have the potential to provide useful information for the management of patients and hospital resources.

Hepatitis C diagnosis and treatment in people who use drugs: mind the gap in the linkage to care

OBJECTIVE

The objective of this study is to identify a simplified rapid screening and linkage-to-care model for HCV among PWUD.

PATIENTS AND METHODS

The study stems from a collaborative project bringing together two local Italian Centers for Drug Addiction and the Hepatology-Infectious Diseases Department of Lazzaro Spallanzani. A research physician analyzed the available medical records seeking to identify HCV and HIV infected patients in care in the addiction centers. Between March 2018 and January 2020 subjects were selected from among a cohort of 720 PWUD in the two Centers' care. The study comprises three steps: first, screening for HCVAb; second, the linkage to care; third, clinical assessment to treatment. The research physician recruited patients for the first two steps directly in their local addiction center. The third step was conducted in the Spallanzani. The characteristics of those subjects who adhered to the three-step study program were then compared to those of the non-adhering PWUD.

RESULTS

194 were known HCVAb positive patients. Of the 505 PWUD in the care of the two Centers eligible for screening, 364 were enrolled in the study. 144 resulted HCVAb positive. 269 were tested for HCVRNA. 101 underwent a full assessment. 96 patients started antiviral therapy with DAA. Patients who refused first step screening were older patients and mainly heroin users; in the second step, almost all the HIV/HCV co-infected patients agreed to a viremia test; in the third step all the HIV/HCV co-infected patients refused HCV treatment.

CONCLUSIONS

The study suggests an on-site specialist approach conducted directly in the addiction centers themselves starting from screening; it can bring the goal of HCV PWUD microelimination closer.

Convolutional Neural Networks for Automated Classification of Prostate Multiparametric Magnetic Resonance Imaging Based on Image Quality

Background

Prostate magnetic resonance imaging (MRI) is technically demanding, requiring high image quality to reach its full diagnostic potential. An automated method to identify diagnostically inadequate images could help optimize image quality.

Purpose

To develop a convolutional neural networks (CNNs) based analysis pipeline for the classification of prostate MRI image quality.

Study Type

Retrospective.

Subjects

Three hundred sixteen prostate mpMRI scans and 312 men (median age 67).

Field Strength/Sequence

A 3 T; fast spin echo T2WI, echo planar imaging DWI, ADC, gradient‐echo dynamic contrast enhanced (DCE).

Assessment

MRI scans were reviewed by three genitourinary radiologists (V.P., M.D.M., S.C.) with 21, 12, and 5 years of experience, respectively. Sequences were labeled as high quality (Q1) or low quality (Q0) and used as the reference standard for all analyses.

Statistical Tests

Sequences were split into training, validation, and testing sets (869, 250, and 120 sequences, respectively). Inter‐reader agreement was assessed with the Fleiss kappa. Following preprocessing and data augmentation, 28 CNNs were trained on MRI slices for each sequence. Model performance was assessed on both a per‐slice and a per‐sequence basis. A pairwise t‐test was performed to compare performances of the classifiers.

Results

The number of sequences labeled as Q0 or Q1 was 38 vs. 278 for T2WI, 43 vs. 273 for DWI, 41 vs. 275 for ADC, and 38 vs. 253 for DCE. Inter‐reader agreement was almost perfect for T2WI and DCE and substantial for DWI and ADC. On the per‐slice analysis, accuracy was 89.95% ± 0.02% for T2WI, 79.83% ± 0.04% for DWI, 76.64% ± 0.04% for ADC, 96.62% ± 0.01% for DCE. On the per‐sequence analysis, accuracy was 100% ± 0.00% for T2WI, DWI, and DCE, and 92.31% ± 0.00% for ADC. The three best algorithms performed significantly better than the remaining ones on every sequence (P‐value < 0.05).

Data Conclusion

CNNs achieved high accuracy in classifying prostate MRI image quality on an individual‐slice basis and almost perfect accuracy when classifying the entire sequences.

Evidence Level

4

Technical Efficacy

Stage 1

Fecal microbiota signatures of insulin resistance, inflammation, and metabolic syndrome in youth with obesity: a pilot study

AIMS

To identify fecal microbiota profiles associated with metabolic abnormalities belonging to the metabolic syndrome (MS), high count of white blood cells (WBCs) and insulin resistance (IR).

METHODS

Sixty-eight young patients with obesity were stratified for percentile distribution of MS abnormalities. A MS risk score was defined as low, medium, and high MS risk. High WBCs were defined as a count ≥ 7.0 10³/µL; severe obesity as body mass index Z-score ≥ 2 standard deviations; IR as homeostatic assessment model algorithm of IR (HOMA) ≥ 3.7. Stool samples were analyzed by 16S rRNA-based metagenomics.

RESULTS

We found reduced bacterial richness of fecal microbiota in patients with IR and high diastolic blood pressure (BP). Distinct microbial markers were associated to high BP (Clostridium and Clostridiaceae), low high-density lipoprotein cholesterol (Lachnospiraceae, Gemellaceae, Turicibacter), and high MS risk (Coriobacteriaceae), WBCs (Bacteroides caccae, Gemellaceae), severe obesity (Lachnospiraceae), and impaired glucose tolerance (Bacteroides ovatus and Enterobacteriaceae). Conversely, taxa such as Faecalibacterium prausnitzii, Parabacterodes, Bacteroides caccae, Oscillospira, Parabacterodes distasonis, Coprococcus, and Haemophilus parainfluenzae were associated to low MS risk score, triglycerides, fasting glucose and HOMA-IR, respectively. Supervised multilevel analysis grouped clearly "variable" patients based on the MS risk.

CONCLUSIONS

This was a proof-of-concept study opening the way at the identification of fecal microbiota signatures, precisely associated with cardiometabolic risk factors in young patients with obesity. These evidences led us to infer, while some gut bacteria have a detrimental role in exacerbating metabolic risk factors some others are beneficial ameliorating cardiovascular host health.

Assessing the impact of data-driven limitations on tracing and forecasting the outbreak dynamics of COVID-19

The availability of the epidemiological data strongly affects the reliability of several mathematical models in tracing and forecasting COVID-19 pandemic, hampering a fair assessment of their relative performance. The marked difference between the lethality of the virus when comparing the first and second waves is an evident sign of the poor reliability of the data, also related to the variability over time in the number of performed swabs. During the early epidemic stage, swabs were made only to patients with severe symptoms taken to hospital or intensive care unit. Thus, asymptomatic people, not seeking medical assistance, remained undetected. Conversely, during the second wave of infection, total infectives included also a percentage of detected asymptomatic infectives, being tested due to close contacts with swab positives and thus registered by the health system. Here, we compared the outcomes of two SIR-type models (the standard SIR model and the A-SIR model that explicitly considers asymptomatic infectives) in reproducing the COVID-19 epidemic dynamic in Italy, Spain, Germany, and France during the first two infection waves, simulated separately. We found that the A-SIR model overcame the SIR model in simulating the first wave, whereas these discrepancies are reduced in simulating the second wave, when the accuracy of the epidemiological data is considerably higher. These results indicate that increasing the complexity of the model is useless and unnecessarily wasteful if not supported by an increased quality of the available data.

Human Papillomavirus Infections in Cervical Samples From HIV-Positive Women: Evaluation of the Presence of the Nonavalent HPV Genotypes and Genetic Diversity

Non-nonavalent vaccine (9v) Human papillomavirus (HPV) types have been shown to have high prevalence among HIV-positive women. Here, 1444 cervical samples were tested for HPV DNA positivity. Co-infections of the 9v HPV types with other HPV types were evaluated. The HPV81 L1 and L2 genes were used to investigate the genetic variability of antigenic epitopes. HPV-positive samples were genotyped using the HPVCLART2 assay. The L1 and L2 protein sequences were analyzed using a self-optimized prediction method to predict their secondary structure. Co-occurrence probabilities of the 9v HPV types were calculated. Non9v types represented 49% of the HPV infections; 31.2% of the non9v HPV types were among the low-grade squamous intraepithelial lesion samples, and 27.3% among the high-grade squamous intraepithelial lesion samples, and several genotypes were low risk. The co-occurrence of 9v HPV types with the other genotypes was not correlated with the filogenetic distance. HPV81 showed an amino-acid substitution within the BC loop (N75Q) and the FGb loop (T315N). In the L2 protein, all of the mutations were located outside antigenic sites. The weak cross-protection of the 9v types suggests the relevance of a sustainable and effective screening program, which should be implemented by HPV DNA testing that does not include only high-risk types.

Treatment of primary Sjögren's syndrome with hydroxychloroquine: a retrospective, open-label study

Objectives

To determine whether patients with primary Sjögren's syndrome (SS), diagnosed according to San Diego criteria, had improvement in their laboratory or clinical features during treatment with hydroxychloroquine (6–7 mg/kg/day) for at least two years., Methods: The study population included 50 consecutive patients with primary SS who were diagnosed according to San Diego criteria, and in whom hydroxychloroquine was suggested as treatment. This group included 10 patients who were early dropouts (side effects or desire not to take antimalarial drugs) and 40 patients who received drugs for at least two years (range 24-48 months). In a subset of SS patients, values for ESR (westergren) and quantitative immunoglobulins were available for comparison. Improvement with therapy was defined as: (a) ≥20% improvement in variables of tear flow (Schirmer's test I) or corneal integrity (rose Bengal): (b) ≥20% salivary function (flow rate); and (c) improvement in at least two of the following measures: physicians assessment of global disease activity by ≥ 20%, patient assessment of improvement in pain or fatigue by ≥20%, and ESR improved by ≥20mm/ hr.

Results

In a retrospective study of SS patients who completed the trial, a significant improvement was noted in ocular symptoms (pain and dryness) in patients (55 and 57%) and improved corneal integrity (rose Bengal straining) in 53% of patients. The Schirmer's test was improved by ≥ 2 mm/5 minutes in 50% in patients. Improvement was noted in oral symptoms (pain and dryness) in patients (57 and 60%) and salivary flow rate was increased in 82% of patients. In a subset of SS patients evaluated, the ESR improved by ≥20mm/hr in 17/32 patients (53%) and quantitative IgG level by ≥20% in 8/13 patients (61%). Physician global assessment of overall patient status and patient assessment of overall status indicated improvement in over 62% of patients.

Conclusion

In a retrospective study of patients fulfilling San Diego Criteria for SS, we found: (a) sustained improvement of local symptoms (painful eyes, painful mouth) and improvement of systemic manifestations (arthralgias and myalgias) after treatment with hydroxychloroquine 6-7 mg/kg/day over mean three-year follow-up; (b) laboratory analysis showed a significant improvement in their ESR and their quantitative IgG levels; (c) no significant late toxicity was observed in this study cohort. A prospective study of hydroxychloroquine in patients fulfilling San Diego criteria for SS is indicated.

Bioactive compounds from extra virgin olive oils: Correlation between phenolic content and oxidative stress cell protection

When compared with other edible vegetable oils, the extra virgin olive oil (EVOO) exhibits excellent nutritional properties due to the presence of biophenolic compounds. Although they constitute only a very small amount of the unsaponifiable fraction of EVOO, biophenols strongly contribute to the sensorial properties of this precious food conferring it, for example, the bitter or pungent taste. Furthermore, it has been found that biophenols possess beneficial effects against many human pathologies such as oxidative stress, inflammation, cardiovascular diseases, cancer and aging-related illness. In the present work, the biophenolic content of 51 Italian and Spanish EVOOs was qualitatively and quantitatively identified and their antioxidant ability analyzed by oxygen radical absorbance capacity (ORAC) assay. Results indicated that the maximum relationship can be found if the ORAC value is correlated with the concentration of the large family composed by ligstroside and oleuropein derivatives together with their degradation products, hydroxytyrosol and tyrosol. Then, selected biophenolic extracts were tested in NIH-3T3 cell line to verify their ability in the recovery of the oxidative stress revealed by DCFH-DA assay. Results were linearly correlated with the concentration of ligstroside aglycone (aldehyde and hydroxyl form).

Treatment of primary Sjögren's syndrome with hydroxychloroquine: a retrospective, open-label study

OBJECTIVES

To determine whether patients with primary Sjögren's syndrome (SS), diagnosed according to San Diego criteria, had improvement in their laboratory or clinical features during treatment with hydroxychloroquine (6-7 mg/kg/day) for at least two years.

METHODS

The study population included 50 consecutive patients with primary SS who were diagnosed according to San Diego criteria, and in whom hydroxychloroquine was suggested as treatment. This group included 10 patients who were early dropouts (side effects or desire not to take antimalarial drugs) and 40 patients who received drugs for at least two years (range 24-48 months). In a subset of SS patients, values for ESR (westergren) and quantitative immunoglobulins were available for comparison. Improvement with therapy was defined as: (a) > or = 20% improvement in variables of tear flow (Schirmer's test I) or corneal integrity (rose Bengal): (b) > or = 20% salivary function (flow rate); and (c) improvement in at least two of the following measures: physicians assessment of global disease activity by > or = 20%, patient assessment of improvement in pain or fatigue by > or = 20%, and ESR improved by > or = 20 mm/hr.

RESULTS

In a retrospective study of SS patients who completed the trial, a significant improvement was noted in ocular symptoms (pain and dryness) in patients (55 and 57%) and improved corneal integrity (rose Bengal straining) in 53% of patients. The Schirmer's test was improved by > or = 2 mm/5 minutes in 50% in patients. Improvement was noted in oral symptoms (pain and dryness) in patients (57 and 60%) and salivary flow rate was increased in 82% of patients. In a subset of SS patients evaluated, the ESR improved by > or = 20 mm/hr in 17/32 patients (53%) and quantitative IgG level by > or = 20% in 8/13 patients (61%). Physician global assessment of overall patient status and patient assessment of overall status indicated improvement in over 62% of patients.

CONCLUSION

In a retrospective study of patients fulfilling San Diego Criteria for SS, we found: (a) sustained improvement of local symptoms (painful eyes, painful mouth) and improvement of systemic manifestations (arthralgias and myalgias) after treatment with hydroxychloroquine 6-7 mg/kg/day over mean three-year follow-up; (b) laboratory analysis showed a significant improvement in their ESR and their quantitative IgG levels; (c) no significant late toxicity was observed in this study cohort. A prospective study of hydroxychloroquine in patients fulfilling San Diego criteria for SS is indicated.